Adaptive Model of Classification of Professions in Vocational Guidance Systems

Abstract

Vocational guidance is part of psychosocial development and is understood as a method that helps to determine the most appropriate profession according to the aptitudes and abilities of the student. The processes of vocational guidance are dynamic and focus on educating and favoring the decision-making process in the professional choice for a learning pathway throughout the student's life, which will benefit society in the long run. Most of the current solutions, both theoretical and applied, from Europe and North America differ when used in the Colombian context, mainly for adults, since the process of classifying professions is not accurate nor precise. In addition, there are various educational projects and evaluation systems in secondary education level institutions. At this level, the students have a changing vocational choice which implies taking into account specific characteristics of the context, also, the student profile vocational guidance determinants. The objective of this article is to describe the adaptive model of occupational classification integrated into the Intelligent Web Platform used in educational institutions in the Department of Cauca. The use of the CRISP-DM methodology allowed finding the Naive Bayes and Deep learning algorithms as those with the best performance in the classification of professions.